Papers with Code 아카이브
Knowledge Distillation
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Knowledge distillation is the process of transferring knowledge from a large model to a smaller one. While large models (such as very deep neural networks or ensembles of many models) have higher knowledge capacity than small models, this capacity might not be fully utilized.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/icml2024357/hombasis-gnn
https://github.com/ictnlp/Seq-NAT
https://github.com/iejMac/ScriptWriter
Learning Transferable Visual Models From Natural Language Supervision
https://github.com/iirishikaii/cleverhans
Technical Report on the CleverHans v2.1.0 Adversarial Examples Library
https://github.com/iit-pavis/look_around_and_learn
https://github.com/ikostrikov/rlpd
Challenges and Opportunities in Offline Reinforcement Learning from Visual Observations
https://github.com/ilialarchenko/abstract_reasoning
https://github.com/ilias-vrg/ilias
https://github.com/imatif17/Joint-Unsupervised-Domain-adaption-and-Knowledge-distillation
Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
https://github.com/iml-dkfz/sf-visuals
A Call to Reflect on Evaluation Practices for Failure Detection in Image Classification
https://github.com/implus/CarDetectionExample
https://github.com/inmoonlight/confcrawl
Why Skip If You Can Combine: A Simple Knowledge Distillation Technique for Intermediate Layers
https://github.com/innerNULL/simpler-distil-whisper
Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo Labelling
https://github.com/intel/neural-compressor
https://github.com/intellabs/vl-interpret
KD-VLP: Improving End-to-End Vision-and-Language Pretraining with Object Knowledge Distillation
https://github.com/intelligent-computing-lab-yale/spikesim
https://github.com/inyong37/Vision
https://github.com/ipieter/universal-distillation
FairDistillation: Mitigating Stereotyping in Language Models
https://github.com/irfanICMLL/ETC-Real-time-Per-frame-Semantic-video-segmentation
Efficient Semantic Video Segmentation with Per-frame Inference
https://github.com/irfanicmll/torchdistiller
https://github.com/ironbar/AnimalAI-Olympics
https://github.com/iskandari/darknet
https://github.com/istvanmegyeri/audio_tagging
https://github.com/ivMCL/AttentiveNorm_Detection
https://github.com/ivan-chai/torch-linear-assignment
HoTPP Benchmark: Are We Good at the Long Horizon Events Forecasting?
https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning
https://github.com/iyerkrithika21/mesh2ssm_2023
https://github.com/jachiam/cpo
Benchmarking Deep Reinforcement Learning for Continuous Control
https://github.com/jacksonchen1998/Cold-Start-Reinforcement-Learning-with-Softmax-Policy-Gradient
https://github.com/jacobdeasy/flexible-ehr
Multitask learning and benchmarking with clinical time series data
https://github.com/jacobgil/pytorch-grad-cam
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
https://github.com/jacobtyo/mudd
Torchreid: A Library for Deep Learning Person Re-Identification in Pytorch
https://github.com/jajajajaja121/mmdetection
https://github.com/jaketae/pytorch-malware-detection
DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter
https://github.com/james77777778/keras-image-models
GhostNetV3: Exploring the Training Strategies for Compact Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.